1. Field of the Invention
The present invention relates to a method and apparatus for predicting video traffic, and more particularly, to a method and apparatus for predicting video traffic, which can be effectively applied to Moving Picture Experts Group (MPEG)-4 video traffic having variable bit rate (VBR) properties and can thus precisely predict the size of video frames.
The present invention was partly supported by the IT R&D program of Ministry of Information and Communication (MIC) and Institute for Information Technology Advancement (IITA)[Project No.: 2006-S-058-02, Project Title: Development of AII IPv6-Based Fixed-Mobile Convergence Networking Technology]
2. Description of the Related Art
Moving Picture Experts Group (MPEG) standards are international standards that define how to compress and represent multimedia data such as audio/video data and include MPEG-1, MPEG-2 and MPEG-4. MPEG-4 can provide higher compression rates than MPEG-1 and MPEG-2. In addition, MPEG-4 treats object elements such as frames and voice data independently and can thus allow users to freely configure frames and voice data.
MPEG-4 video data is classified into an Intra-frame (I-frame), a Predictive-frame (P-frame) and a Bidirectional-Predictive-frame (B-frame). An I-frame is encoded independently of its previous and subsequent frames. A P-frame is encoded or decoded with reference to its previous I-frame or P-frame. That is, a P-frame is encoded in such a manner that the difference between a previous frame and a current frame can be encoded. A B-frame is encoded with reference to at least one of its previous and subsequent P frames.
MPEG video data has a group of pictures (GOP), which is a sequence of frames including P and B frames between a pair of adjacent I-frames. The GOP pattern of MPEG video data is as follows: IBBPBBPBBPBB.
Since MPEG video traffic generally has variable bit rate (VBR) properties and is highly dependent upon the content of video data, traffic bursts are highly likely to occur during the transmission of MPEG video traffic. Such traffic bursts cause transmission delays and deterioration in the performance of communication and network systems.
Therefore, in order to efficiently transmit MPEG video traffic, research has been conducted on ways to model and predict the properties of MPEG video traffic, and various MPEG video traffic prediction methods such as a least mean square (LMS) method and a neural network (NN) method, which is based on artificial intelligence (AI) technology have been developed. These conventional video traffic prediction methods, however, involve very complicated computation processes and are thus difficult to be implemented as communication and network equipment. In addition, these conventional video traffic prediction methods tend to result in high prediction error rates.